High Precision Temperature Research Articles

Robustly packaged lithium niobate resonator-based temperature sensor

Abstract Whispering gallery mode (WGM) resonators with high optical quality factor (Q) are highly sensitive to environment changes and are used in various sensing applications. We have developed a high-precision temperature sensor using a compactly packaged lithium niobate (LN) WGM resonator. This sensor offers a sensitivity of 34.38pm/℃ and a high resolution of up to 5.6×10-4 ℃. It is portable with high Q, making it suitable for using outside the laboratory. The device is also resilient to changes in humidity, enhancing its practicality.

Experimental study on high-precision measurement of surface temperature in friction stir welding aluminium alloy 2219 based on infrared thermometry

In friction stir welding (FSW) of aluminium alloy 2219, the welding temperature critically influences the weld quality. Accurate measurement of the welding temperature is essential for effective welding process control. However, the conventional approach of non-contact infrared thermometry faces significant challenges in FSW due to the inherently low and variable emissivity of aluminium alloys, making it susceptible to environmental radiation interference. This paper proposes a novel, high-precision method for measuring the surface temperature of FSW aluminium alloy 2219 based on infrared thermometry principles. Initially, the study delves into these principles, analysing factors that affect the accuracy of infrared thermometry in the context of FSW in industrial settings. To enhance the surface emissivity of the weldments, a high-emissivity coating is applied to the aluminium alloy surface. Additionally, strategic adjustments in the positioning of the thermal imager and careful selection of the measuring area effectively mitigate interference from the FSW tool’s radiation. Conventional methods report a temperature measurement error greater than 2%, while the proposed method reduces this error to 1.2%. The effectiveness and practical utility of the method are validated through FSW experiments on flat plates and rocket tank rings, corroborated by comparative analyses with surface thermocouple temperature measurements. This confirms the viability of the proposed method for high-precision temperature measurement in aluminium alloy FSW applications.

Temperature-compensated optical fiber sensor for urea detection based on the femtosecond laser-inscribed process

This study demonstrates a low crosstalk temperature-compensated fiber-based urea sensor based on the hybridization of surface plasmon resonance (SPR) and Mach-Zehnder interferometer (MZI). In this sensing structure, an inscribed waveguide and a semi-open cavity are processed by a femtosecond laser in an optical fiber to excite an SPR signal sensitive to refractive index (RI) and an MZI signal highly sensitive to temperature. Based on the temperature detected by the MZI, the influence of temperature on the SPR signal’s RI detection can be calculated and compensated for using a sensitivity matrix. Based on this precise RI detection of the SPR signal, we have achieved selective urea detection by bio-functionalizing the sensor surface using the urease self-assembly method. The sensitivity and limit of detection for urea are 7.98 (nm/mmol/L) and 0.13 mmol/L, respectively, which are better than those in previous studies. Compared to previous temperature compensation studies, this SPR + MZI sensing method using femtosecond laser processing significantly improves the temperature sensitivity of the MZI signal, which reduces crosstalk and ensures a sufficient working range for the SPR signal. Therefore, this sensor is crucial for advancing high-precision temperature compensation detection, high-sensitivity biological detection, and the future application of the femtosecond laser technology.

High-Resolution Infrared Reflectance Distribution Measurement Under Variable Temperature Conditions.

The bidirectional reflectance distribution function (BRDF) can effectively characterize the reflectance properties of a target, which can be used to correct infrared remote sensing data and improve the accuracy of remote sensing measurements. When the surface temperature changes, the reflectance characteristics of the target usually change, and it is necessary to carry out BRDF measurements under variable temperature conditions. In this paper, a variable-temperature infrared BRDF measurement system based on a robotic arm is developed to realize high-resolution wide-temperature region measurement of BRDF. To improve the measurement accuracy, the shaping optical path was used to expand the laser beam, combined with the laser level to accurately adjust the three-dimensional coordinates of the robotic arm, and the dichotomy method is used to calibrate the detector nonlinearly. A portable heater suitable for the mechanical arm corner mechanism is developed, and fast and high-precision temperature control is realized by proportional integral derivative (PID) control. The specular and diffuse BRDF distributions were measured at room temperature to verify the effectiveness of the system. The BRDF distribution of SUS314 stainless steel samples with different roughness is measured during two temperature increases from 20 °C to 1000 °C, and the changing rule of BRDF under variable temperature environment is summarized, which provides technical support for evaluating the optical properties of high-temperature materials and improving the measurement accuracy of remote sensing data.

Temperature Compensation Method for Tunnel Magnetoresistance Micro-Magnetic Sensors Through Reference Magnetic Field.

The sensitivity of Tunnel Magnetoresistance (TMR) sensors is characterized by significant temperature drift and poor sensitivity drift repeatability, which severely impairs measurement accuracy. Conventional temperature compensation techniques are often hindered by low compensation precision, inadequate real-time performance, and an inability to effectively address the issue of poor repeatability in temperature drift characteristics. To overcome these challenges, this paper introduces a novel method for suppressing temperature drift in TMR sensors. In this method, an alternating reference magnetic field is applied to TMR sensors, and the output amplitude at the frequency of the reference magnetic field is calculated to compensate the sensitivity temperature drift in real time. Temperature characteristic tests were conducted in a non-magnetic temperature test chamber, and the results revealed that the proposed method significantly reduced the TMR sensitivity drift coefficient from 985.39 ppm/°C to 59.08 ppm/°C. Additionally, the repeatability of sensitivity temperature characteristic curves was enhanced, with a reduction in root mean square error from 0.84 to 0.21. This approach effectively mitigates temperature-induced sensitivity drift without necessitating the use of a temperature sensor, and has the advantages of real-time performance and repeatability, providing a new approach for the high-precision temperature drift suppression of TMR.

Design and verification of high-precision dynamic temperature control system

In view of the fact that the current temperature environment simulation equipment is difficult to realize the temperature offset test of temperature-sensitive scientific payloads in deep space exploration, a high-precision dynamic temperature control system was designed and built, and then tested and verified. The system uses liquid nitrogen cold plate as the cold source, and realizes high-precision static 300 mm × 300 mm and dynamic temperature control on the contact surface of the payload through electric heating temperature control, heat pipe heat transfer and firmware heat conduction. The test results show that in the case of scientific payload-10 ∘C ∼ 45 ∘CWithin the working temperature range, the contact surface between the temperature control system and the scientificpayload can achieve high-precision temperature stability control, and the temperature fluctuation is better than ± 0.001 ∘C; at the same time, it can also realize ± 0.05 ∘C/1000 s ∼ 1 ∘C/1000 slinear control of the heating and cooling rate of the scientific payload contact surface. Compared with the temperature curve set by the temperature offset test, the maximum errors of the whole temperaturetracking are 0.003 ∘C and respectively 0.04 ∘C . The established temperature control system has the ability of high-precision temperature dynamic offset test, and has considerable application prospects in tasks such as deep space exploration payload development test.

New calibration equation of NTC thermistor for high-precision temperature measurement in narrow temperature ranges

The study proposed a new equation of NTC thermistor which was suitable for efficient calibration in narrow temperature ranges. The equation required only two calibration points to achieve millikelvin(mK) level temperature measurement in narrow temperature range of 5 degrees Celsius. To confirm the performance of the equation, fourteen NTC thermistors were calibrated in the temperature ranges of −5 to 0 ℃, 0 to 5 ℃, and 20 to 25 ℃, respectively. The results demonstrated that the new equation achieved average residuals of 0.28 mK in the narrow temperature range of 5 degrees Celsius. Additionally, the deviation from the commonly used Hoge equation for NTC thermistors was within 0.60 mK. These findings validated the feasibility of using two calibration points to achieve high-precision temperature measurement in narrow temperature ranges and offered a new approach for high-precision temperature measurement.

High-Resolution Reconstruction of Temperature Fields Based on Improved ResNet18.

High-precision measurement of temperature value distributions in production scenarios is of great significance for industrial production, but traditional temperature field reconstruction algorithms rely on the design of manual feature extraction methods with high computational complexity and poor generalization ability. In this paper, we propose a high-precision temperature field reconstruction algorithm based on deep learning, using an efficient adaptive feature extraction method for temperature field reconstruction. We design an improved temperature field reconstruction algorithm based on the ResNet18 neural network; introduce the CBAM attention mechanism in the model; and design a feature pyramid, using M-FPN, a multi-scale feature aggregation network fusing PAN and FPN, to make the extracted feature information propagate multi-dimensionally among different layers to improve the feature characterization ability. Finally, the mean square error is used to guide the model to optimize the training so that the model pays more attention to the data and reduces the large error to ensure that the gap between the predicted value and the real value is small. The experimental results show that the reconstruction accuracy of the improved algorithm presented in this paper is significantly better than that of the original algorithm in the case of typical peaked temperature field distributions.

The growth mechanism and optical properties of flower-like porous Gd2O2S:Er3+/Yb3+ phosphor

The self-assembly mechanism of porous materials has always been a hot topic and a challenging issue in research. What factors induce the self-assembly process, and how is the temperature sensing performance related to it? Innovatively, in this paper, Gd2O2S:Er3+/Yb3+ phosphors with different morphologies were synthesized, and elucidated the formation mechanism of flower-like structures through relevant theorems, formulas, and experiments, proposing that polar molecules and dipole-dipole interactions guide the synergistic effect of van der Waals forces and hydrogen bonding, which ultimately induces the self-assembly of spherical structures into flower-like ones. In addition, four representative intermediates were selected to investigate their luminescence and temperature sensing properties. The results show that the flower-like sample can respond more effectively to changes in ambient temperature because of its rich pore structures, and thus exhibit better temperature sensing performance. In-depth research on the self-assembly mechanism of materials helps to predict and regulate their properties, and the unique characteristics of flower-like sample provide new ideas for material selection in the field of high-precision temperature measurement.

Research on ultra-high temperature sensor and cold end compensation error

Abstract An ultra high temperature sensor and cold end compensation error are proposed. Aiming at the ultra-high temperature and relatively complex harsh environment of spacecraft, a sensor probe structure which can withstand the high temperature of 2500°C is presented in this paper. According to the practical application characteristics of the sensor with high measurement accuracy, the author proposed a high-precision cold end compensation method. Through this compensation technology, the ultra-high temperature sensor maintains high-precision temperature measurement in the full temperature range. The simulation analysis of the sensor structure and temperature test show that the sensor can not only realize temperature measurement at ultra-high temperature, but also control the nonlinear error to 0.6% of the full scale. The accuracy of the whole temperature range is controlled within 0.92% of the full scale, which meets the practical requirements.

Implementation of Wearable Wireless Chest Patch Monitoring Terminal

A wearable wireless chest patch monitoring terminal is designed to realize the acquisition, processing, and wireless transmission of ECG, respiration, and body temperature signals. The analog front-end ADS1292R, which integrates respiratory impedance and ECG front-end, is utilized to collect human ECG and respiratory signals. The body temperature is collected using a low-power, high-precision digital temperature sensor MAX30208. A filter algorithm for signal processing and wireless transmission is designed through a low-power nRF52840 Bluetooth SoC with an Arm Cortex-M4F kernel. The experimental results show that the designed monitoring terminal can monitor the ECG, respiration, and body temperature parameters of the human body in real-time and send the monitoring results via Bluetooth, with a continuous working time of more than 13 hours. The wearable wireless chest patch monitoring terminal features good portability, long standby time, and high measurement accuracy, and it has promising application prospects in the fields of family health monitoring, mobile medical treatment, and smart healthcare.

Experimental Study of Fiber-Optic Temperature Sensor Based on Dual FSIs

To improve the sensitivity measurement of temperature sensors, a fiber optic temperature sensor structure based on the harmonic Vernier effect with two parallel fiber Sagnac interferometers (FSIs) is designed, and theoretical analysis and experimental testing are conducted. The FSI consisting of two polarization maintaining fibers (PMFs) with lengths of 13.62 m and 15.05 m respectively is used to achieve the basic Vernier effect. Then by changing the length of one PMF to approximately i times that of the others, the FSI composed of two PMFs of 7.1 m and 15.05 m is used to achieve the first-order harmonic Vernier effect. Afterward, temperature sensing tests are conducted to observe the wavelength drift during temperature changes and ultimately achieve high sensitivity. The experimental results show that the temperature sensitivity of the sensor based on the first-order harmonic Vernier effect is −28.89 nm/°C, which is 17.09 times that of a single FSI structure (−1.69 nm/°C) and 1.84 times that of the sensitivity generated by the structure based on the basic Vernier effect (−15.69 nm/°C). The experimental results are consistent with the theoretical analysis. The structure proposed in this paper achieves drift measurement of 0.1 °C variation based on 1 °C drift, making the fiber optic temperature sensor applicable to related fields that require high precision temperature. The proposed temperature sensor has the simple structure, low production cost, high sensitivity, and broad application prospects.

Improving the ERA5-Land Temperature Product through a Deep Spatiotemporal Model That Uses Fused Multi-Source Remote Sensing Data

Temperature is a crucial indicator for studying climate, as well as the social and economic changes in a region. Temperature reanalysis products, such as ERA5-Land, have been widely used in studying temperature change. However, global-scale temperature reanalysis products have errors because they overlook the influence of multiple factors on temperature, and this issue is more obvious in smaller areas. During the cold months (January, February, March, November, and December) in the Yellow River Basin, ERA5-Land products exhibit significant errors compared to temperatures observed by meteorological stations, typically underestimating the temperature. This study proposes improving temperature reanalysis products using deep learning and multi-source remote sensing and geographic data fusion. Specifically, convolutional neural networks (CNN) and bidirectional long short-term memory networks (BiLSTM) capture the spatial and temporal relationships between temperature, DEM, land cover, and population density. A deep spatiotemporal model is established to enhance temperature reanalysis products, resulting in higher resolution and more accurate temperature data. A comparison with the measured temperatures at meteorological stations indicates that the accuracy of the improved ERA5-Land product has been significantly enhanced, with the mean absolute error (MAE) reduced by 28.7% and the root mean square error (RMSE) reduced by 25.8%. This method obtained a high-precision daily temperature dataset with a 0.05° resolution for cold months in the Yellow River Basin from 2015 to 2019. Based on this dataset, the annual trend of average temperature changes during the cold months in the Yellow River Basin was analyzed. This study provides a scientific basis for improving ERA5-Land temperature reanalysis products in the Yellow River Basin and offers theoretical support for climate change research in the region.

High-precision temperature control of helium Joule–Thomson cryocooler: Long-term temperature stability

To meet the refrigeration requirements of scientific instruments for mK-level temperature stability over a long-time range in the liquid helium temperature, the temperature characteristics and high-precision control strategies of the helium Joule-Thomson cryocooler (JTC) have been experimentally investigated for the first time. Based on the Ideal Gas Law, the influencing mechanism of pre-cooling temperature, gas reservoir volume, and liquid phase volume fraction on refrigeration temperature were qualitatively analyzed and experimentally verified. An active control studies of pre-cooling temperature, low-pressure and heat load were carried out to verify the feasibility of active temperature control of helium JTC. Finally, a long-term temperature stability of ±1 mK/h was successfully achieved when a multi-stage active control strategy was adopted, which is the optimal long-term temperature stability of helium JTC that have been reported to date.

Determination of Turbulent Prandtl Number for Thermal Fluid Dynamics Simulation of HVAC Unit by Data Assimilation

Abstract Regarding high-precision temperature field prediction of a heating ventilation and air conditioning (HVAC) unit using thermal fluid dynamics simulation, the determination of the turbulent Prandtl number (Prt) was a key issue. In this study, we present an attempt to improve the accuracy of thermal fluid dynamics simulations in HVAC units by adjusting the Prt using data assimilation techniques. First, we simultaneously measured the velocity and temperature in the hot and cold air mixing zone of a simple HVAC model using particle image velocimetry and thermocouples. Second, we coupled data assimilation to the thermal fluid simulation model to determine the Prt in the mixing field. Finally, we proposed two functions of the Prt for high velocity and low velocity regions using multidimensional analysis. In the future, we believe that the Prt functions can be applied to thermal fluid simulations of actual HVAC unit to accurately predict performance without conducting prototype experiments, thereby contributing to reducing development cost and time of HVAC unit.

Commercial nanodiamonds for precise fluorescence-based temperature sensing

Nanothermometry is crucial for understanding physical, chemical, and biological systems, which require precise temperature measurement. Fluorescent nanodiamonds containing nitrogen-vacancy (NV) color centers offer an approach to temperature sensing. In this study, we present the spectrofluorometric characteristics of the NV zero-phonon lines (575 and 637 nm), in 100 nm nanodiamonds in aqueous volume ensembles at a concentration of 0.5 mg/ml, across the temperature range of 30–45 °C. The NV0 and NV− fluorescence intensities achieved high linear correlation values of 0.99 (INV0) and 0.94 (INV−), respectively, demonstrating their efficiency in high precision temperature assessment. Additionally, we explore NV0 width as temperature increases, NV populations intensity ratios, and INV0/INV− ratios to gain insights into thermal quenching phenomena in fluorescent nanodiamonds, where upon heating, an increasing trend for INV−/(INV0+INV−) is observed, while an antisymmetric effect takes place for INV0/(INV0+INV−). These findings indicate the potential of commercial nanodiamonds for precise all-optical fluorescence-based temperature sensing.

Explainable and generalizable AI-driven multiscale informatics for dynamic system modelling

Ultra-precision machining requires system modelling that both satisfies explainability and conforms to data fidelity. Existing modelling approaches, whether based on data-driven methods in present artificial intelligence (AI) or on first-principle knowledge, fall short of these qualities in high-demanding industrial applications. Therefore, this paper develops an explainable and generalizable ‘grey-box’ AI informatics method for real-world dynamic system modelling. Such a grey-box model serves as a multiscale ‘world model’ by integrating the first principles of the system in a white-box architecture with data-fitting black boxes for varying hyperparameters of the white box. The physical principles serve as an explainable global meta-structure of the real-world system driven by physical knowledge, while the black boxes enhance local fitting accuracy driven by training data. The grey-box model thus encapsulates implicit variables and relationships that a standalone white-box model or black-box model fails to capture. Case study on an industrial cleanroom high-precision temperature regulation system verifies that the grey-box method outperforms existing modelling methods and is suitable for varying operating conditions.

Smith GA-PID feedback control of hot strip final rolling temperature

Abstract At present, the hot strip finishing temperature control system generally adopts a computer model or mechanism model to predict the strip’s final rolling temperature, which is open-loop control. To realize the precise control of the final rolling temperature of high-performance steel, this paper introduces the PID feedback control strategy at the end of the finishing zone, to realize the high-precision temperature control of the strip steel. The Smith compensator is used to predict the compensation for the large hysteresis problem of the final rolling temperature, and various intelligent algorithms are used to optimize the three parameters of the PID controller, eliminate the nonlinear problem, and obtain better dynamic performance. After the experiment, it is concluded that the Smith GA-PID optimization control strategy system has a small overshoot, the peak time and regulation time are the shortest, and the dynamic response is the fastest. Compared with the traditional PID control method, the overshoot is reduced by 97.6%, and the required regulation time is reduced by 7.85 s.

Implementation of Linear Regression Method for Calibration and Temperature Measurement on PT100 Temperature Sensor

The PT100 temperature sensor, a type RTD (resistance temperature detector), is widely employed in industries, particularly for steam boiler temperature regulation. Precise temperature measurement is vital for operational efficiency and safety maintenance. Utilizing linear regression for calibrating and measuring temperature with the PT100 sensor integrated with Arduino Uno and LCD I2C is proposed to enhance accuracy. Testing involved assembling the PT100 sensor with Arduino Uno and LCD I2C. Test data were employed for linear regression to derive the calibration equation. Measurements were conducted by comparing PT100 sensor readings with a digital thermometer across a temperature range of 30ºC to 75ºC at 5ºC intervals, with each temperature point tested five times. Results exhibit good accuracy in temperature measurement with the PT100 sensor, featuring low error and percentage error. The standard deviation indicates consistent measurement. Integration of the PT100 temperature sensor with Arduino Uno and LCD I2C, coupled with linear regression for calibration, yields a precise temperature measurement system. Testing against a digital thermometer demonstrates highly accurate outcomes. This developed system finds application in high-precision temperature measurement for steam boiler operations.

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.